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Playing Text-Adventure Games with Graph-Based Deep Reinforcement Learning

arXiv 1812.01628 ↗First posted 4 Dec 2018cs.CL cs.AI cs.LGcited by 53

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Abstract (arXiv)

Text-based adventure games provide a platform on which to explore reinforcement learning in the context of a combinatorial action space, such as natural language. We present a deep reinforcement learning architecture that represents the game state as a knowledge graph which is learned during exploration. This graph is used to prune the action space, enabling more efficient exploration. The question of which action to take can be reduced to a question-answering task, a form of transfer learning that pre-trains certain parts of our architecture. In experiments using the TextWorld framework, we show that our proposed technique can learn a control policy faster than baseline alternatives. We have also open-sourced our code at https://github.com/rajammanabrolu/KG-DQN.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Dec 2018.

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Citing papers per year: 2021: 16; 2022: 10; 2023: 11; 2024: 7; 2025: 6; 2026: 3.

53 papers.

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  1. 2023A Long Way to Go: Investigating Length Correlations in RLHFcited by 285
  2. 2022Pre-Trained Language Models for Interactive Decision-Makingcited by 220
  3. 2024Teaching Large Language Models to Reason with Reinforcement Learningcited by 158
  4. 2023Learning to Model the World with Languagecited by 66
  5. 2021What Would Jiminy Cricket Do? Towards Agents That Behave Morallycited by 57
  6. 2022Improving Intrinsic Exploration with Language Abstractionscited by 49
  7. 2023Learning to Generate Better Than Your LLMcited by 49
  8. 2024Werewolf Arena: A Case Study in LLM Evaluation via Social Deductioncited by 42
  9. 2022On Transforming Reinforcement Learning by Transformer: The Development Trajectorycited by 35
  10. 2021Learning Knowledge Graph-based World Models of Textual Environmentscited by 31
  11. 2021Neuro-Symbolic Reinforcement Learning with First-Order Logiccited by 30
  12. 2022Multi-Stage Episodic Control for Strategic Exploration in Text Gamescited by 22
38 more
  1. 2023Automated Cyber Defence: A Reviewcited by 19
  2. 2022Reinforcement learning on graphs: A surveycited by 18
  3. 2021A Systematic Survey of Text Worlds as Embodied Natural Language Environmentscited by 17
  4. 2021A Survey of Text Games for Reinforcement Learning informed by Natural Languagecited by 14
  5. 2021Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learningcited by 14
  6. 2022TextWorldExpress: Simulating Text Games at One Million Steps Per Secondcited by 12
  7. 2022Learning to Follow Instructions in Text-Based Gamescited by 12
  8. 2021Modeling Worlds in Textcited by 11
  9. 2021Generalization in Text-based Games via Hierarchical Reinforcement Learningcited by 11
  10. 2022Perceiving the World: Question-guided Reinforcement Learning for Text-based Gamescited by 10
  11. 2023Knowledge-enhanced Agents for Interactive Text Gamescited by 10
  12. 2021Situated Dialogue Learning through Procedural Environment Generationcited by 9
  13. 2025TALES: Text Adventure Learning Environment Suitecited by 9
  14. 2021Case-based Reasoning for Better Generalization in Textual Reinforcement Learningcited by 8
  15. 2023Dialogue Shaping: Empowering Agents through NPC Interactioncited by 8
  16. 2021Detecting and Adapting to Novelty in Gamescited by 7
  17. 2023Story Shaping: Teaching Agents Human-like Behavior with Storiescited by 7
  18. 2025Large Language Models Think Too Fast To Explore Effectivelycited by 7
  19. 2025Web World Modelscited by 7
  20. 2026Group-Graph Policy Optimization for Long-Horizon Agentic Reinforcement Learningcited by 7
  21. 2021LOA: Logical Optimal Actions for Text-based Interaction Gamescited by 6
  22. 2023Thespian: Multi-Character Text Role-Playing Game Agentscited by 6
  23. 2021Eye of the Beholder: Improved Relation Generalization for Text-based Reinforcement Learning Agentscited by 4
  24. 2023Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learningcited by 4
  25. 2024EXPLORER: Exploration-guided Reasoning for Textual Reinforcement Learningcited by 3
  26. 2024Knowing What Not to Do: Leverage Language Model Insights for Action Space Pruning in Multi-agent Reinforcement Learningcited by 3
  27. 2021Situated Language Learning via Interactive Narrativescited by 2
  28. 2021Interactive Machine Comprehension with Dynamic Knowledge Graphscited by 2
  29. 2021Learning Natural Language Generation from Scratchcited by 2
  30. 2023Learn What Is Possible, Then Choose What Is Best: Disentangling One-To-Many Relations in Language Through Text-based Gamescited by 2
  31. 2024A Mixture-of-Experts Approach to Few-Shot Task Transfer in Open-Ended Text Worldscited by 2
  32. 2025Learning Natural Language Constraints for Safe Reinforcement Learning of Language Agentscited by 2
  33. 2025Vejde: A Framework for Inductive Deep Reinforcement Learning Based on Factor Graph Color Refinementcited by 2
  34. 2022An Analysis of Deep Reinforcement Learning Agents for Text-based Gamescited by 1
  35. 2023Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Gamescited by 1
  36. 2024Towards Goal-Oriented Agents for Evolving Problems Observed via Conversationcited by 1
  37. 2024Positive Experience Reflection for Agents in Interactive Text Environmentscited by 1
  38. 2022DiffG-RL: Leveraging Difference between State and Common Sensecited by 0

50 most cited of 53 All 53 (JSON)